A modified two-stage SVM-RFE model for cancer classification using microarray data

Authors:
Phit Ling Tan;Shing Chiang Tan;Chee Peng Lim;Swee Eng Khor
Affiliations:
Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia;Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia;School of Computer Sciences, University of Science Malaysia, Penang, Malaysia;Mimos Berhad, Kulim Hi-Tech Park, Kulim, Kedah, Malaysia
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Year:
2011

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Abstract

Gene selection is one of the research issues for improving classification of microarray gene expression data. In this paper, a gene selection algorithm, which is based on the modified Recursive Feature Elimination (RFE) method, is integrated with a Support Vector Machine (SVM) to build a hybrid SVM-RFE model for cancer classification. The proposed model operates with a two-stage gene elimination scheme for finding a subset of expressed genes that indicate a disease. The effectiveness of the proposed model is evaluated using a multi-class lung cancer problem. The results show that the proposed SVM-RFE model is able to perform well with high classification accuracy rates.